Supplier selection quantitative evaluation method

Abstract

The invention belongs to the technical field of supply chain management, and particularly relates to a supplier selection quantitative evaluation method. According to the method, a standardized supplier selection quantitative evaluation model is constructed by adopting an analytic hierarchy process, a Sazel 1-9 scaling method and an entropy weight method, and accurate selection of high-quality suppliers is realized through multi-step scientific calculation and grading. According to the method, rich and in-depth information support is provided for follow-up evaluation on the basis of comprehensively mastered relevant specific conditions, and the false association risk is reduced; preferential strategies of different dimensions are flexibly combined for specific projects to adapt to diversified requirements, so that the professionality and pertinence of evaluation are improved; according to the method, based on the clear selection key point weight judgment matrix, the selection key point weight calculation logic, the key element score standardization calculation logic, the selection key point score calculation logic and the comprehensive score calculation logic, the interpretability of the decision-making process in practical application is improved through the transparent decision-making logic.

Description

Technical Field

[0001] This invention belongs to the field of supply chain management technology, specifically relating to a quantitative evaluation method for supplier selection. Background Technology

[0002] For large enterprises or those with large procurement volumes, procurement management is a crucial link, directly impacting cost control and supply chain stability. Traditional supplier evaluation often relies on personal experience or simple indicators, making it difficult to comprehensively and objectively assess supplier capabilities. Therefore, multi-dimensional, precise quantitative supplier evaluation methods are increasingly gaining attention. Currently, research on quantitative supplier evaluation has made some progress. Chinese patent CN120598398B discloses a method, system, equipment, medium, and program product for evaluating bulk raw material suppliers. This evaluation method collects raw relevant data from multiple sample suppliers based on multi-source heterogeneous data sources, preprocesses the raw relevant data to obtain sample data, performs static analysis on sample suppliers based on business demand information to generate static evaluation parameters, and then constructs an initial evaluation model based on these parameters. By monitoring supplier profiles, business data table structures, and significant market changes, changes to static evaluation dimensions are automatically triggered. Simultaneously, using data acquired through real-time data acquisition interfaces and combined with a dynamic weight allocation algorithm, the weight vectors of each static evaluation dimension are automatically adjusted, thereby adjusting and optimizing the initial evaluation model to obtain a target evaluation model. The data of the suppliers to be analyzed is then input into the target evaluation model for evaluation. Chinese patent CN118446782B discloses an intelligent procurement management method and system based on collaborative analysis of supplier information. The method includes: acquiring basic supplier information; acquiring material procurement needs, screening supplier qualification information, and obtaining multiple qualified suppliers based on the screening results; acquiring a set of past transaction records, conducting supply stability assessment, ranking suppliers, and establishing an initial supplier sequence; searching in a price database to obtain the expected procurement price; acquiring multiple quotation information and calculating the deviation coefficient from the expected procurement price to obtain multiple price deviation coefficients; and adjusting the initial supplier sequence to obtain a final supplier sequence.

[0003] The aforementioned existing technical solutions provide a quantitative reference benchmark with multi-dimensional data support for supplier evaluation, marking a step towards data-driven management in supplier evaluation. However, existing technologies still reveal some limitations in practical applications. First, relying on automatically captured supplier data from ERP, CRM systems, and third-party platforms as the basis for supplier evaluation, while involving multiple data aspects, still falls short in terms of information mining. For example, it lacks consideration of details such as product expertise, manufacturing processes, and after-sales systems, and may not effectively distinguish between the supplier's true capabilities and the influence of external confounding factors. For instance, seemingly excellent delivery records may not stem from the supplier's own efficiency but merely from the accidental occurrence of specific favorable conditions. Such evaluations based on false correlations carry significant risks and uncertainties. Second, relying primarily on evaluation equipment for data processing and model building may lead to... Lack of professionalism and specificity in assessment: Different procurement needs vary. For example, some urgent orders prioritize delivery time, while those with tight cash flow may focus more on payment terms. However, assessment equipment relies on limited rule settings for data processing and model building, thus lacking the flexibility to combine different priority strategies to adapt to diverse needs. Third, the existing system's model building and assessment process are very complex, and the decision-making logic is usually opaque. The scores or rankings they provide are closed to users due to the complex calculation process behind them, making it impossible to explain the specific reasons for supplier ranking. This lack of interpretability in the decision-making process severely restricts its application depth in engineering procurement, which requires high trust and risk auditing. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the technical problem this invention aims to solve is to provide a quantitative evaluation method for supplier selection. This method is based on a comprehensive understanding of relevant specific circumstances during supplier selection evaluation, providing rich and in-depth information support for subsequent evaluations. It identifies key selection points for specific projects, improving the flexibility and relevance of the evaluation, and employs transparent decision-making logic to enhance the interpretability of the decision-making process in practical applications.

[0005] This invention comprehensively utilizes the Analytic Hierarchy Process (AHP), the Satie 1-9 scale, and the entropy weight method to construct a standardized quantitative evaluation model for supplier selection. Through multi-step scientific calculation and grading, it achieves accurate selection of high-quality suppliers. The core selection model is as follows:

[0006] The supplier's overall score S = Σ (selection point weight × selection point score). This model multiplies the selection point weight and the selection point score and then sums them up. The selection point weight is obtained based on the Satie 1-9 scale method. The selection point score is obtained based on the entropy weight method.

[0007] Includes the following steps:

[0008] Step S1: Obtain product line information;

[0009] Preferably, in step S1, the product line information obtained includes relevant product expertise, production equipment, manufacturing processes, cost structure, marketing model, after-sales system, qualifications and performance, market share, and entry and exit mechanism information.

[0010] Step S2, Model Structure Design;

[0011] Preferably, in step S2, the model structure design includes setting the selection criteria and their corresponding key elements to form a supplier selection scheme as follows:

[0012] The selection criteria include: basic information about the supplier (A1), price and cost (A2), delivery capability (A3), quality of goods or services (A4), and market conditions (A5).

[0013] The key elements of supplier basic information (A1) include qualifications (B1), performance (B2), production line (B3), minimum product lifespan (B4), raw materials (B5), and production capacity (B6); the key elements of price and cost (A2) include quoted price (B7), total cost (B8), and gross profit (B9); the key elements of delivery capability (A3) include delivery time (B10) and logistics (B11); the key elements of service quality (A4) include the number of service outlets (B12), after-sales response time (B13), and after-sales service (B14); and the key elements of market situation (A5) include cooperation in research (B15), willingness to participate (B16), and brand recommendation rate (B17).

[0014] The 11 quantitative key elements mentioned above include: performance (B2), production line (B3), minimum product lifespan (B4), production capacity (B6), quoted price (B7), total cost (B8), gross profit (B9), delivery time (B10), number of service outlets (B12), after-sales response time (B13), and brand recommendation ratio (B17). Among them, performance (B2) is the fulfillment rate, production line (B3) is the number of production lines, production capacity (B3) is the annual output, and brand recommendation ratio (B17) is the proportion of the number of times the supplier's brand is recommended to the purchaser by a third-party platform to the total number of times all supplier brands in the same category are recommended during the same period.

[0015] The six qualitative key elements mentioned above are: Qualifications (B1), Raw Materials (B5), Logistics and Distribution (B11), After-sales Service (B14), Survey Cooperation (B15), and Participation Willingness (B16). Each element includes the following breakdown: Qualifications (B1) includes: number of months of business license validity, number of violations in the past 36 months, certification rate of technical personnel, number of patents and technical certifications, and years of industry experience; Raw Materials (B5) includes: pass rate, defect rate, raw material inventory turnover rate, and frequency of supply disruption risk; Logistics and Distribution (B11) includes: on-time delivery rate, goods accuracy rate, delivery address accuracy rate, and customer complaint rate; After-sales Service (B14) includes: problem resolution rate and number of certified service personnel; Survey Cooperation (B15) includes: average response time, information accuracy rate, number of proactively provided information items, and number of times participating in survey discussions; Participation Willingness (B16) includes: number of proactive communications, amount of human resources invested, and amount of material resources invested.

[0016] Step S3: Determine the weight of the selection criteria;

[0017] Preferably, step S3 includes:

[0018] Step S3.1: Comparison of the importance of each selection point

[0019] Based on the selection criteria, pairwise comparisons are performed to construct a weighted judgment matrix 'a', in the following form:

[0020]

[0021] In the matrix, n represents the number of selection criteria, and element a ij This represents the relative importance of selection point Ai compared to selection point Aj, i.e., "importance of Ai / importance of Aj". Correspondingly, a ji For a ij The reciprocal of a, after determining a ij After that, a ji It can be directly exported because it has the same importance as the others, so a 11 a 22 a 33 ... a nn The values ​​are all 1;

[0022] Step S3.2: Quantify the relative importance among the selection criteria.

[0023] The relative importance of different selection criteria was quantified using the Satie 1-9 scale.

[0024] Step S3.3: Calculate the weights of the selection criteria.

[0025] Preferably, step S3.3 includes:

[0026] Calculate the weight vector of the selection criteria. The calculation logic for the weights of the selection criteria is as follows:

[0027] (1) Calculate the product of the elements in each row of matrix a.

[0028] For the weight judgment matrix a constructed in step S3.1, calculate the product M of the elements in each row. i :

[0029]

[0030] in These are the elements in matrix a;

[0031] (2) Calculate the nth root

[0032] The value of n is the number of selection criteria, based on M. i Calculate its nth root :

[0033]

[0034] (3) Normalization

[0035] The calculated nth root is normalized to obtain the weight vector W. i :

[0036] .

[0037] Step S4: Determine the standard scores for key elements;

[0038] Preferably, step S4 includes:

[0039] Step S4.1, organize the data

[0040] For each key element, the data obtained in step S1 is organized. Quantitative key elements have clear numerical expressions and can be directly applied to subsequent calculations. For qualitative key elements, the decomposed contents they cover are sorted out and scored.

[0041] Step S4.2, Standardization of Key Element Scores

[0042] Distinguish between benefit-oriented and cost-oriented key elements, and calculate the standardized scores of specific suppliers for each key element;

[0043] For benefit-related key factors, the higher the better, the better; for cost-related key factors, the lower the better, the better.

[0044] Key factors for profitability include: qualifications (B1), performance (B2), production line (B3), minimum product lifespan (B4), raw materials (B5), production capacity (B6), total cost (B8), logistics and distribution (B11), number of service outlets (B12), after-sales service (B14), cooperation in surveys (B15), willingness to participate (B16), and brand recommendation rate (B17); key factors for cost include: quoted price (B7), gross profit margin (B9), delivery time (B10), and after-sales response time (B13).

[0045] The calculation logic for the standardized key element scores is as follows:

[0046] (1) For key elements of efficiency, the formula is used.

[0047]

[0048] (2) For cost-type key elements, the formula is used.

[0049]

[0050] Where i represents the supplier, j represents the key element, and X ij Z represents the data of the i-th supplier in the j-th key element. ij Let $X$ be the standardized score of the $i$-th supplier in the $j$-th key element, min(X$) j ) represents the minimum data for all suppliers in this key element, max(X) j This represents the maximum data for all suppliers in this key element.

[0051] Step S5: Determine the scores for the selection criteria;

[0052] Preferably, in step S5, the score of the specific supplier at each selection point is calculated, and the calculation logic for the selection point score is as follows:

[0053] After confirming the scores of the key elements included in each selection point, the scores of the key elements under the same selection point are added together to obtain the score of that selection point.

[0054] Step S6: Determine the overall score;

[0055] Preferably, in step S6, the comprehensive score of a specific supplier is calculated, and the calculation logic for the comprehensive score is as follows:

[0056] Supplier overall score S = Σ (selection criteria weight × selection criteria score)

[0057] The final comprehensive score for each supplier is obtained by multiplying the weight of the selection criteria by the score of the selection criteria and summing the results.

[0058] Step S7, grading.

[0059] Preferably, in step S7, after ranking the suppliers according to their comprehensive scores, the suppliers are divided into four levels, as follows:

[0060] Tier I: Top suppliers, ranked in the top 10% (inclusive);

[0061] Level II: Good suppliers, ranking in the top 10% to 30% (inclusive);

[0062] Tier III: General suppliers, ranking in the top 30% to 60% (inclusive);

[0063] Level IV: Risky suppliers, ranking in the top 60% to 100% (inclusive).

[0064] The beneficial effects obtained by adopting the above technical solution are as follows:

[0065] (1) By mastering the relevant product knowledge, production equipment, manufacturing process, cost structure, marketing model, after-sales system, qualifications and performance, market share and entry and exit mechanism, etc., we have provided rich and in-depth information support for subsequent evaluation, reduced the risk of false association, and helped to select high-quality suppliers more accurately.

[0066] (2) For specific projects, a supplier selection plan is formulated, and the selection criteria, levels and corresponding key elements are set to ensure that they meet the actual needs. Different priority strategies can be flexibly combined to adapt to diverse needs, which improves the professionalism and pertinence of the evaluation.

[0067] (3) It has a clear selection point weight judgment matrix, selection point weight calculation logic, key element score standardization calculation logic, selection point score calculation logic and comprehensive score calculation logic, and adopts transparent decision-making logic, which improves the interpretability of the decision-making process in practical applications. Detailed Implementation

[0068] The technical solution of the present invention will now be described more clearly and completely.

[0069] Taking the idler roller distribution project as an example, the technical solution of this invention is implemented in detail as follows:

[0070] 1. Model Structure Design

[0071] Idler rollers produced by different suppliers vary in manufacturing processes and material selection, resulting in differences in performance. Furthermore, their application scenarios are diverse, and customers have varying preferences regarding brands and services. The final selection criteria include the following five dimensions: supplier basic information (A1), price and cost (A2), delivery capability (A3), product or service quality (A4), and market conditions (A5).

[0072] Regarding the supplier's basic information (A1), the evaluation is based on six key elements: qualifications (B1), performance (B2), production line (B3), minimum product lifespan (B4), raw materials (B5), and production capacity (B6).

[0073] Regarding price and cost (A2), the evaluation is based on three key elements: quoted price (B7), total cost (B8), and gross profit (B9).

[0074] Regarding delivery capability (A3), it is evaluated based on two key elements: delivery time (B10) and logistics and distribution (B11).

[0075] Regarding service quality (A4), it is evaluated based on three key elements: number of service outlets (B12), after-sales response time (B13), and after-sales service (B14).

[0076] Regarding the market situation (A5), the evaluation is based on three key elements: survey cooperation (B15), willingness to participate (B16), and brand recommendation rate (B17).

[0077] Specific model examples are as follows:

[0078] Table 1 - Summary of Selection Criteria and Key Elements

[0079]

[0080] 2. Determine the weight of selection criteria.

[0081] (1) Construct the weight judgment matrix

[0082] The basic information about the idler roller supplier (A1) is slightly more important than price and cost (A2), therefore a 12 =3, correspondingly a 21 =1 / 3, forming the complete weight judgment matrix:

[0083]

[0084] (2) Calculate the weight vector

[0085] Step 1: Calculate the product of the elements in each row of matrix a.

[0086] For the constructed weight judgment matrix a, calculate the product M of the elements in each row. i :

[0087]

[0088] In this example,

[0089] M1 = 1×3×3×4×5 = 180

[0090] M2 = (1 / 3)×1×3×5×2 = 10

[0091] M3 = (1 / 3) × (1 / 3) × 1 × 5 × 5 ≈ 2.7778

[0092] M4 = (1 / 4) × (1 / 5) × (1 / 5) × 1 × (1 / 2) ≈ 0.005

[0093] M5 = (1 / 5) × (1 / 2) × (1 / 5) × 2 × 1 ≈ 0.04

[0094] M i The statement is as follows:

[0095] M i = [M1, M2, M3, M4, M5]

[0096] Step 2: Calculate the nth root

[0097] The value of n is the number of selection criteria, based on M. i Calculate its nth root .

[0098]

[0099] In this example,

[0100]

[0101] Step 3: Normalization

[0102] The calculated nth root is normalized to obtain the weight vector W. i .

[0103]

[0104] The total sum is: 2.8252 + 1.5849 + 1.2267 + 0.3466 + 0.5253 = 6.5087

[0105] W1 = 2.8252 / 6.5087 = 0.4341

[0106] W2 = 1.5849 / 6.5087 = 0.2435

[0107] W3 = 1.2267 / 6.5087 = 0.1885

[0108] W4 = 0.3466 / 6.5087 = 0.0532

[0109] W5 = 0.5253 / 6.5087 = 0.0807

[0110] In the idler roller distribution project, the weight vector of the selection criteria is:

[0111] W = [W1, W2, W3, W4, W5]

[0112] = [0.4341, 0.2435, 0.1885, 0.0532, 0.0807]

[0113] 3. Determine the standard scores for key elements.

[0114] (1) Organizing data

[0115] Taking the 13 suppliers participating in the idler roller project as an example, the raw data statistics of key elements are as follows:

[0116] Table 2 - Summary of Supplier Data

[0117]

[0118]

[0119] Table 2 - Supplier Data Summary (Continued)

[0120]

[0121] Table 2 - Supplier Data Summary (Continued)

[0122]

[0123] Based on the data in Table 2, we can obtain the following: 11 quantitative key factors, including performance, production line, product lifespan, production capacity, quoted price, total cost, gross profit, delivery time, number of service outlets, after-sales response time, and brand recommendation rate; and 6 qualitative key factors, including qualifications, raw materials, logistics and distribution, after-sales service, survey cooperation, and willingness to participate.

[0124] Quantitative key elements refer to key elements that can be directly measured by specific numbers or numerical indicators. These numbers can be directly used for scoring calculations, and the results are usually objective and quantifiable.

[0125] Qualitative key elements refer to those that lack specific numerical measurement. It is necessary to sort out the possible breakdown of each qualitative key element and assign scores based on the supplier's performance in this field, combined with actual needs and industry standards.

[0126] The six qualitative key elements mentioned above are: Qualifications (B1), Raw Materials (B5), Logistics and Distribution (B11), After-sales Service (B14), Survey Cooperation (B15), and Participation Willingness (B16). Each element includes the following breakdown: Qualifications (B1) includes: number of months of business license validity, number of violations in the past 36 months, certification rate of technical personnel, number of patents and technical certifications, and years of industry experience; Raw Materials (B5) includes: pass rate, defect rate, raw material inventory turnover rate, and frequency of supply disruption risk; Logistics and Distribution (B11) includes: on-time delivery rate, goods accuracy rate, delivery address accuracy rate, and customer complaint rate; After-sales Service (B14) includes: problem resolution rate and number of certified service personnel; Survey Cooperation (B15) includes: average response time, information accuracy rate, number of proactively provided information items, and number of times participating in survey discussions; Participation Willingness (B16) includes: number of proactive communications, amount of human resources invested, and amount of material resources invested.

[0127] (2) Standardization of Key Element Scores

[0128] For benefit-related key factors, the higher the better, the better; for cost-related key factors, the lower the better, the better.

[0129] Key factors for profitability include: qualifications (B1), performance (B2), production line (B3), minimum product lifespan (B4), raw materials (B5), production capacity (B6), total cost (B8), logistics and distribution (B11), number of service outlets (B12), after-sales service (B14), cooperation in surveys (B15), willingness to participate (B16), and brand recommendation rate (B17); key factors for cost include: quoted price (B7), gross profit margin (B9), delivery time (B10), and after-sales response time (B13).

[0130] For key efficiency factors, the formula is as follows:

[0131]

[0132] For cost-critical elements, the formula is as follows:

[0133]

[0134] Where i represents the supplier, j represents the key element, and X ij Z represents the data of the i-th supplier in the j-th key element. ijLet represent the standardized score of the i-th supplier in the j-th key element. min(X) j ) represents the minimum data for all suppliers in this key element, max(X) j This represents the maximum data for all suppliers in this key element.

[0135] Standardized scores for key performance indicators (taking qualifications and performance as examples):

[0136] i. Qualifications (B1, j=1)

[0137] min(X1)=70, max(X1)=90.

[0138] Z 供应商1-1 = (90-70) / (90-70) = 1

[0139] Z 供应商2-1 = (85-70) / (90-70) = 0.75

[0140] Z 供应商3-1 = (90-70) / (90-70) = 1

[0141] Z 供应商4-1 = (70-70) / (90-70) = 0

[0142] Z 供应商5-1 = (80-70) / (90-70) = 0.5

[0143] Z 供应商6-1 = (75-70) / (90-70) = 0.25

[0144] Z 供应商7-1 = (70-70) / (90-70) = 0

[0145] Z 供应商8-1 = (83-70) / (90-70) = 0.65

[0146] Z 供应商9-1 = (77-70) / (90-70) = 0.35

[0147] Z 供应商10-1 = (73-70) / (90-70) = 0.15

[0148] Z 供应商11-1 = (76-70) / (90-70) = 0.3

[0149] Z 供应商12-1 = (86-70) / (90-70) = 0.8

[0150] Z供应商13-1 =(72 - 70) / (90 - 70)= 0.1

[0151] ii. Performance (B2, j = 2)

[0152] min(X2)= 2345, max(X2)= 6000.

[0153] Z 供应商1-2 =(5000 - 2345) / (6000 - 2345)= 0.7264

[0154] Z 供应商2-2 =(4500 - 2345) / (6000 - 2345)= 0.5896

[0155] Z 供应商3-2 =(6000 - 2345) / (6000 - 2345)= 1

[0156] Z 供应商4-2 =(3000 - 2345) / (6000 - 2345)= 0.1792

[0157] Z 供应商5-2 =(3500 - 2345) / (6000 - 2345)= 0.3160

[0158] Z 供应商6-2 =(3200 - 2345) / (6000 - 2345)= 0.2339

[0159] Z 供应商7-2 =(3000 - 2345) / (6000 - 2345)= 0.1792

[0160] Z 供应商8-2 =(4209 - 2345) / (6000 - 2345)= 0.51

[0161] Z 供应商9-2 =(3178 - 2345) / (6000 - 2345)= 0.2279

[0162] Z 供应商10-2 =(3912 - 2345) / (6000 - 2345)= 0.4287

[0163] Z 供应商11-2 =(2345 - 2345) / (6000 - 2345)= 0

[0164] Z 供应商12-2 =(4530 - 2345) / (6000 - 2345)= 0.5978

[0165] Z 供应商13-2 = (2856 - 2345) / (6000 - 2345) = 0.1398

[0166] Standardized scores for key cost elements (taking price and delivery time as examples):

[0167] i. Quoted amount (B7, j=7)

[0168] min(X7)=2797.38, max(X7)=6487.46.

[0169] Z 供应商1-7 = (6487.46 - 4929.77) / (6487.46 - 2797.38) = 0.4221

[0170] Z 供应商2-7 = (6487.46 - 4887.62) / (6487.46 - 2797.38) = 0.4336

[0171] Z 供应商3-7 = (6487.46 - 6360.7) / (6487.46 - 2797.38) = 0.0344

[0172] Z 供应商4-7 = (6487.46 - 5500) / (6487.46 - 2797.38) = 0.2676

[0173] Z 供应商5-7 = (6487.46 - 4854.25) / (6487.46 - 2797.38) = 0.4426

[0174] Z 供应商6-7 = (6487.46 - 3804.21) / (6487.46 - 2797.38) = 0.7272

[0175] Z 供应商7-7 = (6487.46 - 6000) / (6487.46 - 2797.38) = 0.1321

[0176] Z 供应商8-7 = (6487.46 - 6487.46) / (6487.46 - 2797.38) = 0

[0177] Z 供应商9-7 = (6487.46 - 2797.38) / (6487.46 - 2797.38) = 1

[0178] Z 供应商10-7= (6487.46 - 5555.55) / (6487.46 - 2797.38) = 0.2525

[0179] Z 供应商11-7 = (6487.46 - 5291.2) / (6487.46 - 2797.38) = 0.3242

[0180] Z 供应商12-7 = (6487.46 - 4999.98) / (6487.46 - 2797.38) = 0.4031

[0181] Z 供应商13-7 = (6487.46 - 4921.87) / (6487.46 - 2797.38) = 0.4243

[0182] ii. Delivery period (B10, j = 10)

[0183] min(X 10 ) = 7, max(X 10 ) = 30.

[0184] Z 供应商1-10 = (30 - 15) / (30 - 7) = 0.6522

[0185] Z 供应商2-10 = (30 - 18) / (30 - 7) = 0.15217

[0186] Z 供应商3-10 = (30 - 20) / (30 - 7) = 0.4348

[0187] Z 供应商4-10 = (30 - 7) / (30 - 7) = 1

[0188] Z 供应商5-10 = (30 - 8) / (30 - 7) = 0.9565

[0189] Z 供应商6-10 = (30 - 10) / (30 - 7) = 0.8696

[0190] Z 供应商7-10 = (30 - 15) / (30 - 7) = 0.6522

[0191] Z 供应商8-10 = (30 - 10) / (30 - 7) = 0.8696

[0192] Z 供应商9-10 = (30 - 20) / (30 - 7) = 0.4348

[0193] Z供应商10-10 = (30-25) / (30-7) = 0.2174

[0194] Z 供应商11-10 = (30-30) / (30-7) = 0

[0195] Z 供应商12-10 = (30-15) / (30-7) = 0.6522

[0196] Z 供应商13-10 = (30-30) / (30-7) = 0

[0197] The following are the standardized scores for key elements of all suppliers:

[0198] Table 3 - Standardized Scores of Suppliers in Each Key Element

[0199]

[0200] Table 3 - Standardized Scores of Suppliers in Each Key Element (Continued)

[0201]

[0202] Table 3 - Standardized Scores of Suppliers in Key Elements (Continued)

[0203]

[0204] 4. Determine the scores for key selection criteria.

[0205] The scores of key elements are summarized according to the dimensions of the selection criteria.

[0206] (1) Supplier Basic Information (A1) Score Summary

[0207]

[0208] (2) Price and Cost (A2) Score Summary

[0209]

[0210] 3) Summary of Delivery Capability (A3) Scores

[0211]

[0212] (4) Service Quality (A4) Score Summary

[0213]

[0214] (5) Market Situation (A5) Score Summary

[0215]

[0216] 5. Determine the overall score

[0217] The overall score for each supplier is calculated by combining the weight W of the selection criteria and the score of the selection criteria.

[0218] In the known idler roller distribution project, the weight vector of the selection criteria is:

[0219] W = [0.4341, 0.2435, 0.1885, 0.0532, 0.0807]

[0220]

[0221] 6. Grading

[0222] Suppliers were ranked based on their overall scores, and the results are as follows:

[0223]

Claims

1. A method for quantitatively evaluating a selection of a supplier, characterized by, The method comprises the following steps: Step S1, obtaining product line information; Step S2, designing model structure; Step S3, determining the weight of selection points; Step S4, determining the standard score of key elements; Step S5, determining the score of selection points; Step S6, determining the comprehensive score; Step S7, grading.

2. The evaluation method according to claim 1, characterized in that In the step S1, the obtained product line information comprises relevant product professional knowledge, production equipment, manufacturing process, cost composition, marketing mode, after-sales system, qualification performance, market share, and access and exit mechanism information.

3. The evaluation method according to claim 2, characterized in that In the step S2, the model structure design comprises setting the content of selection points and the content of corresponding key elements, forming a supplier selection scheme as follows: The selection points comprise supplier basic situation (A1), price and cost (A2), delivery capacity (A3), commodity or service quality (A4), and market situation (A5); The key elements of the supplier basic situation (A1) comprise qualification (B1), performance (B2), production line (B3), commodity minimum life (B4), raw material (B5), and production capacity (B6); the key elements of the price and cost (A2) comprise bid amount (B7), total cost (B8), and price gross profit (B9); the key elements of the delivery capacity (A3) comprise delivery period (B10) and logistics distribution (B11); the key elements of the service quality (A4) comprise service network quantity (B12), after-sales response time (B13), and after-sales service (B14); the key elements of the market situation (A5) comprise investigation cooperation degree (B15), participation willingness situation (B16), and brand recommendation proportion (B17); The 11 quantitative key elements in the above key elements comprise performance (B2), production line (B3), commodity minimum life (B4), production capacity (B6), bid amount (B7), total cost (B8), price gross profit (B9), delivery period (B10), service network quantity (B12), after-sales response time (B13), and brand recommendation proportion (B17), wherein the performance (B2) is a qualified rate of performance, the production line (B3) is a production line quantity, the production capacity (B3) is an annual output, and the brand recommendation proportion (B17) is a ratio of the number of times that a supplier brand is recommended by a third-party platform to a procurement party to the total number of times that all supplier brands of the same product category are recommended in the same period. The six qualitative key elements among the above key elements are qualification (B1), raw materials (B5), logistics distribution (B11), after-sales service (B14), research cooperation degree (B15), and participation willingness condition (B16), each containing the following decomposition contents: qualification (B1) includes: business license validity period month number, illegal record number in the last 36 months, technical personnel certificate holding rate, patent and technology certification item number, and industry experience years; raw materials (B5) includes: qualified rate, defective product rate, raw material inventory turnover rate, and supply risk frequency; logistics distribution (B11) includes: on-time delivery rate, goods accuracy rate, distribution address accuracy rate, and customer complaint rate; after-sales service (B14) includes: problem solving rate and service personnel authentication number; research cooperation degree (B15) includes: average response time, information accuracy rate, active information providing number, and research discussion participation number; participation willingness condition (B16) includes: active communication number, manpower investment number, and material investment amount.

4. The evaluation method according to claim 3, characterized in that The step S3 includes: Step S3.1, comparison of importance between each selection point, The relative importance between the selection points is compared, and a weight judgment matrix a is constructed, which is as follows: In the matrix, n represents the number of selection points, and the element a ij represents the relative importance of the selection point Ai compared with the selection point Aj, i.e., "importance of Ai / importance of Aj". Correspondingly, a ji is the reciprocal of a ij . After a ij is determined, a ji can be directly derived. Since the importance degree is the same when compared with itself, the values of a 11 , a 22 , a 33 ,..., and a nn are all 1. Step S3.2, quantification of relative importance between selection points, The relative importance between different selection points is quantified by using the Satty 1-9 scale method; Step S3.3, calculation of selection point weight.

5. The evaluation method according to claim 4, characterized in that The step S3.3 includes: The weight vector of the selection point is calculated, and the calculation logic of the selection point weight is as follows: (1) Calculate the product of each row element in the matrix a: For the weight judgment matrix a built in step S3.1, the product M of the elements of each row is calculated i : ， wherein are elements in matrix a; (2) Calculate the nth root: The value of n is the number of selection points, according to M i Calculate its n root : ， (3) Normalization processing: The computed nth root is normalized to obtain a weight vector W i : 。 6. The evaluation method according to claim 5, characterized in that The step S4 includes: Step S4.1, data arrangement, For each key element, the data obtained in step S1 is arranged, wherein the quantitative key element has a clear numerical expression and is directly applied to subsequent calculation, and the decomposition contents covered by the qualitative key element are combed and scored; Step S4.2, key element score standardization, The benefit type key element and the cost type key element are distinguished, and the standardization score of each key element of the specific supplier is calculated; The benefit type key element is a positive index, the larger the better, and the cost type key element is a negative index, the smaller the better; The benefit type key elements include: qualification (B1), performance (B2), production line (B3), minimum product life (B4), raw materials (B5), production capacity (B6), total cost (B8), logistics distribution (B11), service network number (B12), after-sales service (B14), research cooperation degree (B15), participation willingness condition (B16), and brand recommendation proportion (B17); the cost type key elements include: bid amount (B7), price gross profit (B9), delivery period (B10), and after-sales response time (B13); The calculation logic of the key element score standardization is as follows: (1) For the benefit type key element, the formula is: ， (2) For the cost type key element, the formula is: ， where i represents the supplier, j represents the key element, X ij represents the data of the ith supplier in the jth key element, Z ij represents the normalized score of the ith supplier in the jth key element, min(X j ) represents the minimum data of all suppliers in the key element, max(X j ) represents the maximum data of all suppliers in the key element.

7. The evaluation method according to claim 6, characterized in that The step S5 includes calculating the score of each selection point of the specific supplier, and the calculation logic of the selection point score is as follows: After confirming the key element score of each selection point, the key element scores under the same selection point are added to obtain the score of the selection point.

8. The evaluation method according to claim 7, characterized in that In the step S6, the comprehensive score of the specific supplier is calculated, and the calculation logic of the comprehensive score is: Supplier comprehensive score S = Σ (selection point weight × selection point score), The selection point weight is multiplied by the selection point score and accumulated to obtain the comprehensive score of each supplier.

9. The evaluation method according to claim 8, characterized in that In the step S7, the suppliers are divided into four levels according to the ranking of the comprehensive score, and the four levels are divided as follows: I level: head supplier, top 10%, including 10%; II level: good supplier, top 10%-30%, including 30%; III level: general supplier, top 30%-60%, including 60%; IV level: risk supplier, top 60%-100%, including 100%.

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